Country Scorecard: USA (USA)

A composite attractiveness score for location decisions (FDI, manufacturing footprint, service hubs), built from six dimensions: macro stability, market size and growth, infrastructure, human-capital and capability signals, governance and business environment, and economic complexity. Each dimension is a cross-country percentile within its latest available year (0-100, higher is better), then the dimensions are averaged using equal (1/6 each) weights. In 2024, USA scores 76.8, ranking #12 of 197 scored economies.

What goes in, and what comes out

The scorecard frames a country the way the World Economic Forum's Global Competitiveness Index (Schwab et al., WEF 2019) and the IFC / World Bank Doing Businessseries (IFC 2020) frame location attractiveness, but uses fully open primary data, discloses every weight, and follows the OECD / JRC Handbook on Constructing Composite Indicators(Nardo, Saisana, Saltelli, Tarantola, Hoffman & Giovannini 2008, OECD/JRC methodology manual) on normalisation, weighting, and missingness: per-indicator min-max-to-percentile normalisation; equal-weighted (or user-supplied) linear aggregation; missingness handled by renormalising weights over observed dimensions; and explicit sensitivity to weight changes via the ?weights=parameter. Economic complexity follows the Atlas of Economic Complexity 2nd edition (Hausmann, Hidalgo, Bustos, Coscia, Simoes & Yildirim 2014, CID/MIT Press). Proprietary composite scores (WEF GCI 4.0, EIU scores, PwC attractiveness indices) are not used. The six dimensions and their indicators are:

• Macro stability (lower-is-better): 5-year standard deviation of CPI inflation and of real GDP growth, plus general-government gross debt as % of GDP. All three indicators from the IMF World Economic Outlook (October 2024 vintage).
• Market size & growth (higher-is-better): GDP at current USD in 2024 and 5-year CAGR 2019-2024. From World Bank WDI indicator NY.GDP.MKTP.CD.
• Infrastructure (higher-is-better): World Bank Logistics Performance Indexoverall score (2022; Arvis et al. 2023) and air-freight tonne-km per USD of GDP, a cargo-density proxy.
• Human capital & capabilities (higher-is-better): high-tech manufactured exports share of manufactured exports (TX.VAL.TECH.MF.ZS) and ICT-service exports share of service exports (BX.GSR.CCIS.ZS). These are capability-revealed proxies; the WDI classroom indicators (tertiary enrolment SE.TER.ENRR, expected years of schooling SE.SCH.LIFE) are not in this workbench slice, so they are substituted with export-composition signals that are well correlated with human capital in the capability- based growth literature (Hausmann, Hwang & Rodrik 2007).
• Governance & business environment(higher-is-better): Worldwide Governance Indicators composite (Kaufmann, Kraay & Mastruzzi 2010, World Bank Policy Research WP 5430). Each country's six WGI estimate series, CC.EST (Control of Corruption), GE.EST (Government Effectiveness), PV.EST (Political Stability/No Violence), RQ.EST (Regulatory Quality), RL.EST (Rule of Law), VA.EST (Voice & Accountability), are averaged at the latest year with at least four of six observed (2023 vintage).
• Economic complexity (higher-is-better): Hidalgo-Hausmann Economic Complexity Index in 2024, computed on CEPII BACI export shares via the spectral-eigenvector method of Hidalgo & Hausmann (2009).

Each indicator is converted to a cross-country percentile (0-100) within its latest year, inverted where lower is better, then averaged inside a dimension. Dimension-level percentiles are finally averaged with the six weights. Default weights are equal (1/6 each); pass?weights=w_macro,w_market,w_infra,w_human,w_gov,w_eci as six non-negative numbers to override (they are renormalised to sum to 1). Countries with fewer than three dimensions observed are dropped from the ranking.

USA versus peers on the six dimensions

The radar maps each country's percentile on each dimension from 0 at the centre to 100 at the edge. The closer a polygon hugs the outer ring, the more attractive that country on a given axis. Peers are auto-selected from the same World Bank income group as the chosen country, picking the economies whose composite scores sit just above and just below the target so the comparison stays on its scale.

Top 30 countries by composite score

Each row shows a country's dimension percentiles (0-100) and the weighted composite. Missing cells mean that dimension is not observed for that country in the latest year; the composite is computed over observed dimensions only, with weights renormalised. Countries with fewer than three observed dimensions are excluded from the ranking.

Trajectory: how USA has moved

The composite score over the last ten years for USA. Market-size and complexity dimensions are recomputed year by year against that year's own cross-section percentile; the single-vintage dimensions (LPI 2022, WGI 2023) are carried forward as constants because those source releases are slow-moving relative to the annual window.

Where USA is strong and where it is weak

The bar below reads USA's percentile (0-100) in each of the six dimensions. Bars in green sit in the top third (≥ 66), blue in the middle third (33-66), and red in the bottom third (< 33). Read as a CEO brief: where can the country credibly compete, and where is the investment thesis fragile.

The global map of composite attractiveness

A world view of the composite score helps spot regional clusters and anti-clusters that country-by-country ranking misses. The choropleth below shades every scored economy by its2024 composite percentile (darker = higher). The orange highlight marks USA. Geographic clustering is diagnostic of which peers a sourcing or footprint decision realistically faces: the OECD-JRC Handbook (Nardo et al. 2008) flags spatial-correlation checks as a standard robustness test for composite indicators.

Top 20 economies: dynamic-composite trajectories, 2015-2024

Figures 1-5 read the latest cross-section. The long-run FDI-screening question, though, is whether a country is rising or fading relative to its peers. To answer that, we isolate the two dimensions that actually vary annually in this workbench's data, market size (WDI GDP current USD) and economic complexity (ECI on CEPII BACI), and plot an equal-weighted dynamic composite on those two for each of the top-20 economies from the 2024 full composite. Slow-moving single-vintage dimensions (WGI, LPI, human capital) are excluded so that flat-vintage values do not wash out real dynamics. The target country (USA) is highlighted.

Who is moving? Five-year shift in dynamic composite

Figure 2 reads a snapshot; Figure 6 plots the path. The rank-change leaderboard below combines the two: for each of the top-20 economies from the 2024 full composite, we take the dynamic-composite percentile five years ago (nearest year in the trajectory window, equal-weight market size and ECI) and compare to 2024. Positive bars are economies climbing on both capability and relative market size; negative bars are economies fading against the cross-section. Tracking shifts matters more than levels for FDI screening (Ramondo, Rodriguez-Clare & Saborio-Rodriguez 2016, AER 106: 3159-3184): a rising mid-ranked economy often offers a better medium-horizon cost trajectory than a stagnating top-quartile incumbent.

Quadrant reads: where does USA sit on scale-by-complexity and scale-by-governance?

The composite in Figures 1-7 collapses six dimensions into a single score; strategy offices often need to read the underlying trade-offs directly. Two scatterplots do that: (a) market scale against economic complexity, the Hausmann-Hidalgo (2011) question of whether a country is big AND capable; and (b) market scale against governance , the Kaufmann-Kraay-Mastruzzi (2010) question of whether a country is big AND well-run. Medians partition each plane into four quadrants; the top-right is 'big and good', top-left is 'big but weak', bottom-right is 'small but capable', bottom-left is 'small and weak'. The target country (USA) is flagged in orange on both planes.

How redundant are the six dimensions? Pairwise correlations across the universe

The OECD/JRC Handbook on Constructing Composite Indicators (Nardo, Saisana, Saltelli, Tarantola, Hoffman & Giovannini 2008, Section 6) flags pairwise correlation between sub-indices as a mandatory step before equal-weighted aggregation: a composite that averages six dimensions which are, in fact, ninety-five per cent collinear is mostly counting one signal six times. The bars below report the Pearson correlation of every pair of dimension percentiles across the 197 scored economies in 2024, ordered from strongest to weakest. High pairs (governance × market, complexity × human capital) tell a strategy desk that swapping weights between those two dimensions barely moves the composite; low pairs (macro × complexity) are genuinely orthogonal signals. Saltelli, Annoni, Azzini, Campolongo, Ratto & Tarantola (2010, Computer Physics Communications 181(2): 259-270) on global sensitivity analysis is the classical companion reading.

How strategy offices use this

• FDI and footprint screening. Filter the top-30 table to high governance and infrastructure percentiles for a capital-intensive plant; to high human-capital and complexity percentiles for a tech hub; to high market-size and growth percentiles for a consumer-facing expansion.
• Peer benchmarking for offers and incentives. The radar diagnoses where the target country has a credible edge (outer-ring dimensions) and where it is asking a buyer to take on policy or capability risk (inner-ring dimensions). Use the weakest axis as the negotiation lever for incentive design.
• Reweighting for specific theses. Pass ?weights=w_macro,w_market,w_infra,w_human,w_gov,w_eci to change the composite (e.g. ?weights=0,2,1,1,2,0 for a market-plus-governance thesis). Weights are renormalised. No proprietary weights or opaque expert surveys enter here.

References

• Arvis, J.-F., Ojala, L., Shepherd, B., Ulybina, D. & Wiederer, C. (2023). Connecting to Compete 2023: Trade Logistics in the Global Economy. World Bank. (Logistics Performance Index methodology.)
• Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., Simoes, A., & Yildirim, M. A. (2014). The Atlas of Economic Complexity: Mapping Paths to Prosperity, 2nd edition. CID / MIT Press. (ECI construction and interpretation.)
• Hausmann, R., Hwang, J. & Rodrik, D. (2007). 'What You Export Matters.' Journal of Economic Growth 12(1): 1-25.
• Hidalgo, C. A. & Hausmann, R. (2009). 'The Building Blocks of Economic Complexity.' PNAS 106(26): 10570-10575. (ECI construction.)
• IFC / World Bank (2020). Doing Business 2020: Comparing Business Regulation in 190 Economies. World Bank Group. (EODB score methodology; final release year used here.)
• International Monetary Fund (2024). World Economic Outlook Database, October 2024. (CPI, real GDP growth, general-government gross debt.)
• Kaufmann, D., Kraay, A. & Mastruzzi, M. (2010). 'The Worldwide Governance Indicators: Methodology and Analytical Issues.' World Bank Policy Research Working Paper 5430. (Six-pillar composite used here as the governance dimension.)
• Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., & Giovannini, E. (2008). Handbook on Constructing Composite Indicators: Methodology and User Guide. OECD / JRC. (Normalisation, weighting, missingness, and sensitivity standards.)
• Schwab, K. et al. (2019). The Global Competitiveness Report 2019. World Economic Forum. (Framing; proprietary GCI scores are not used.)
• World Bank. World Development Indicators. (GDP at current USD, logistics, high-tech and ICT export shares, air freight.)